private void ReportPerHla(OptimizationParameterList qmrrParams, Dictionary <string, BestSoFar <double, TrueCollection> > peptideToBestHlaAssignmentSoFar,
string directory, string name)
{
string fileName = string.Format(@"{0}\NoisyOr.PeptideHlaProbability.{1}.new.txt", directory, name);
using (StreamWriter output = File.CreateText(fileName))
{
output.WriteLine(SpecialFunctions.CreateTabString("Peptide", "HLA", "LogOdds", "Probability"));
foreach (QmrrPartialModel qmrrPartialModel in QmrrPartialModelCollection)
{
QmrrModelMissingAssignment aQmrrModelMissingAssignment = QmrrModelMissingAssignment.GetInstance(ModelLikelihoodFactories, qmrrPartialModel, qmrrParams);
BestSoFar <double, TrueCollection> bestHlaAssignment = peptideToBestHlaAssignmentSoFar[qmrrPartialModel.Peptide];
Set <Hla> trueCollectionFullAsSet = new Set <Hla>(bestHlaAssignment.Champ);
double loglikelihoodFull = bestHlaAssignment.ChampsScore;
foreach (Hla hla in trueCollectionFullAsSet)
{
bool known = qmrrPartialModel.KnownHlaSet.Contains(hla);
if (!known)
{
Set <Hla> allLessOne = trueCollectionFullAsSet.SubtractElement(hla);
TrueCollection trueCollectionWithout = TrueCollection.GetInstance(allLessOne);
double loglikelihoodWithout = aQmrrModelMissingAssignment.LogLikelihoodOfCompleteModelConditionedOnKnownHlas(trueCollectionWithout);
double logOdds = loglikelihoodFull - loglikelihoodWithout;
double probabilityFull = Math.Exp(loglikelihoodFull);
double probabilityWithout = Math.Exp(loglikelihoodWithout);
double probability = probabilityFull / (probabilityFull + probabilityWithout);
Debug.Assert(logOdds >= 0); // real assert
output.WriteLine(SpecialFunctions.CreateTabString(qmrrPartialModel.Peptide, hla, logOdds, probability));
}
}
}
}
}
public static QmrrModelMissingAssignment GetInstance(ModelLikelihoodFactories modelLikelihoodFactories, QmrrPartialModel qmrrPartialModel, OptimizationParameterList qmrrParams)
{
QmrrModelMissingAssignment aQmrrModelMissingAssignment = new QmrrModelMissingAssignment();
aQmrrModelMissingAssignment.QmrrPartialModel = qmrrPartialModel;
aQmrrModelMissingAssignment.OptimizationParameterList = qmrrParams;
aQmrrModelMissingAssignment.KnownHlaSet = qmrrPartialModel.KnownHlaSet;
aQmrrModelMissingAssignment.SwitchableHlasOfRespondingPatients = qmrrPartialModel.SwitchableHlasOfRespondingPatients;
aQmrrModelMissingAssignment.ModelLikelihoodFactories = modelLikelihoodFactories;
aQmrrModelMissingAssignment.LogLikelihoodOfCompleteModelConditionedOnKnownHlas = modelLikelihoodFactories.MissingAssignmentDelegateFactory(qmrrPartialModel, qmrrParams);
return(aQmrrModelMissingAssignment);
//SetOfAllHlasCount = qmrrPartialModel.HlaList.Count;
}
protected QmmrModelOnePeptide QmmrModelOnePeptideGetInstance(QmrrPartialModel qmrrPartialModel, OptimizationParameterList qmrrParams, double depth)
{
QmmrModelOnePeptide aQmmrModelOnePeptide = new QmmrModelOnePeptide();
aQmmrModelOnePeptide.QmrrModelMissingAssignment = QmrrModelMissingAssignment.GetInstance(ModelLikelihoodFactories, qmrrPartialModel, qmrrParams);
aQmmrModelOnePeptide.CreateNoSwitchablesHlaAssignment();
if (depth == 0)
{
// do nothing
}
else if (depth == Math.Floor(depth))
{
SpecialFunctions.CheckCondition(depth > 0);
aQmmrModelOnePeptide.SetForDepthSearch((int)Math.Floor(depth));
}
else
{
SpecialFunctions.CheckCondition(depth == 1.5);
aQmmrModelOnePeptide.SetForBitFlipsAnd1Replacement();
}
return(aQmmrModelOnePeptide);
}
